[GitHub] [incubator-tvm] jwfromm commented on a change in pull request #5441: Add TopK to ONNX Frontend

[GitBox <gi...@apache.org>]

jwfromm commented on a change in pull request #5441:
URL: https://github.com/apache/incubator-tvm/pull/5441#discussion_r414874263



##########
File path: python/tvm/relay/frontend/onnx.py
##########
@@ -1470,6 +1470,23 @@ def _impl_v9(cls, inputs, attr, params):
         output = AttrCvt(op_name='argwhere')(inputs, attr, params)
         return _op.transpose(output, axes=(1, 0))
 
+class TopK(OnnxOpConverter):
+    """Operator converter for TopK
+    """
+    @classmethod
+    def _impl_v1(cls, inputs, attr, params):
+        if len(inputs) != 2:
+            raise ValueError("Expect 2 input only")
+        inp = inputs[0]
+        axis = attr.get("axis", len(infer_shape(inp)) - 1)
+        largest = attr.get("largest", 1)
+
+        if largest == 0:
+            raise ValueError("TVM only supports finding TopK largest elements")

Review comment:
       maybe assert that `largest > 0` to catch negative cases as well.

##########
File path: python/tvm/relay/frontend/onnx.py
##########
@@ -1470,6 +1470,23 @@ def _impl_v9(cls, inputs, attr, params):
         output = AttrCvt(op_name='argwhere')(inputs, attr, params)
         return _op.transpose(output, axes=(1, 0))
 
+class TopK(OnnxOpConverter):
+    """Operator converter for TopK
+    """
+    @classmethod
+    def _impl_v1(cls, inputs, attr, params):
+        if len(inputs) != 2:
+            raise ValueError("Expect 2 input only")
+        inp = inputs[0]
+        axis = attr.get("axis", len(infer_shape(inp)) - 1)

Review comment:
       why not use `-1` as axis instead of inferring shape here?

##########
File path: tests/python/frontend/onnx/test_forward.py
##########
@@ -2330,6 +2330,44 @@ def verify_nonzero(indata, outdata, dtype):
     result = np.array((np.nonzero(input_data)))  # expected output [[0, 1, 2, 2], [0, 1, 0, 1]]
     verify_nonzero(input_data, result, dtype=np.int64)
 
+def test_topk():
+    def verify_topk(input_dims, K, axis=-1):
+        output_dims = list(input_dims)
+        output_dims[axis] = K
+
+        node = helper.make_node('TopK',
+                                inputs=['X', 'K'],
+                                outputs=['Values', 'Indicies'],
+                                axis=axis)
+
+        graph = helper.make_graph([node],
+                                  "topk_test",
+                                  inputs=[helper.make_tensor_value_info("X", TensorProto.FLOAT, list(input_dims)),
+                                          helper.make_tensor_value_info("K", TensorProto.INT64, [1,])],
+                                  initializer=[helper.make_tensor("K", TensorProto.INT64, [1], [K])],
+                                  outputs=[helper.make_tensor_value_info("Values", TensorProto.FLOAT, output_dims), 
+                                           helper.make_tensor_value_info("Indicies", TensorProto.INT64, output_dims)])
+
+        model = helper.make_model(graph, producer_name='topk_test')
+
+        indata = np.random.uniform(-10, 10, input_dims).astype(np.float32)
+        onnx_out = get_onnxruntime_output(model, [indata, k])
+        print(onnx_out)
+
+        for target, ctx in [('llvm', tvm.cpu())]:
+            tvm_out = get_tvm_output(model, indata, target, ctx, [output_dims, output_dims], 
+                    output_dtype=['float32', 'int64'])
+            print(tvm_out)
+            tvm.testing.assert_allclose(onnx_out, tvm_out, rtol=1e-05, atol=1e-05)
+    
+    for shape in [[10], [10, 10], [10, 10, 10]]:
+        for k in [1, 5, 10]:
+            verify_topk(shape, k)
+
+    verify_topk([10,10,10], 5, 0)
+    verify_topk([10,10,10], 5, 1)
+    verify_topk([10,10,10], 5, 2)

Review comment:
       It's probably worth testing at least one other input dimension.




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